def test_ica_reset():
"""Test ICA resetting"""
raw = Raw(raw_fname).crop(0.5, stop, False)
raw.load_data()
picks = pick_types(raw.info, meg=True, stim=False, ecg=False,
eog=False, exclude='bads')[:10]
run_time_attrs = (
'_pre_whitener',
'unmixing_matrix_',
'mixing_matrix_',
'n_components_',
'n_samples_',
'pca_components_',
'pca_explained_variance_',
'pca_mean_'
)
with warnings.catch_warnings(record=True):
ica = ICA(
n_components=3, max_pca_components=3, n_pca_components=3,
method='fastica', max_iter=1).fit(raw, picks=picks)
assert_true(all(hasattr(ica, attr) for attr in run_time_attrs))
ica._reset()
assert_true(not any(hasattr(ica, attr) for attr in run_time_attrs))
def test_ica_reset(method):
"""Test ICA resetting."""
_skip_check_picard(method)
raw = read_raw_fif(raw_fname).crop(0.5, stop).load_data()
picks = pick_types(raw.info,
meg=True,
stim=False,
ecg=False,
eog=False,
exclude='bads')[:10]
run_time_attrs = ('pre_whitener_', 'unmixing_matrix_', 'mixing_matrix_',
'n_components_', 'n_samples_', 'pca_components_',
'pca_explained_variance_', 'pca_mean_')
with pytest.warns(UserWarning, match='did not converge'):
ica = ICA(n_components=3,
max_pca_components=3,
n_pca_components=3,
method=method,
max_iter=1).fit(raw, picks=picks)
assert (all(hasattr(ica, attr) for attr in run_time_attrs))
assert ica.labels_ is not None
ica._reset()
assert (not any(hasattr(ica, attr) for attr in run_time_attrs))
assert ica.labels_ is not None
def test_ica_reset(method):
"""Test ICA resetting."""
_skip_check_picard(method)
raw = read_raw_fif(raw_fname).crop(0.5, stop).load_data()
picks = pick_types(raw.info, meg=True, stim=False, ecg=False,
eog=False, exclude='bads')[:10]
run_time_attrs = (
'_pre_whitener',
'unmixing_matrix_',
'mixing_matrix_',
'n_components_',
'n_samples_',
'pca_components_',
'pca_explained_variance_',
'pca_mean_'
)
with warnings.catch_warnings(record=True): # convergence
ica = ICA(
n_components=3, max_pca_components=3, n_pca_components=3,
method=method, max_iter=1).fit(raw, picks=picks)
assert_true(all(hasattr(ica, attr) for attr in run_time_attrs))
assert_not_equal(ica.labels_, None)
ica._reset()
assert_true(not any(hasattr(ica, attr) for attr in run_time_attrs))
assert_not_equal(ica.labels_, None)
def test_ica_reset(method):
"""Test ICA resetting."""
_skip_check_picard(method)
raw = read_raw_fif(raw_fname).crop(0.5, stop).load_data()
picks = pick_types(raw.info, meg=True, stim=False, ecg=False,
eog=False, exclude='bads')[:10]
run_time_attrs = (
'pre_whitener_',
'unmixing_matrix_',
'mixing_matrix_',
'n_components_',
'n_samples_',
'pca_components_',
'pca_explained_variance_',
'pca_mean_'
)
with pytest.warns(UserWarning, match='did not converge'):
ica = ICA(
n_components=3, max_pca_components=3, n_pca_components=3,
method=method, max_iter=1).fit(raw, picks=picks)
assert (all(hasattr(ica, attr) for attr in run_time_attrs))
assert ica.labels_ is not None
ica._reset()
assert (not any(hasattr(ica, attr) for attr in run_time_attrs))
assert ica.labels_ is not None
def test_ica_reset(method):
"""Test ICA resetting."""
_skip_check_picard(method)
raw = read_raw_fif(raw_fname).crop(0.5, stop).load_data()
picks = pick_types(raw.info,
meg=True,
stim=False,
ecg=False,
eog=False,
exclude='bads')[:10]
run_time_attrs = ('_pre_whitener', 'unmixing_matrix_', 'mixing_matrix_',
'n_components_', 'n_samples_', 'pca_components_',
'pca_explained_variance_', 'pca_mean_')
with warnings.catch_warnings(record=True): # convergence
ica = ICA(n_components=3,
max_pca_components=3,
n_pca_components=3,
method=method,
max_iter=1).fit(raw, picks=picks)
assert_true(all(hasattr(ica, attr) for attr in run_time_attrs))
assert_not_equal(ica.labels_, None)
ica._reset()
assert_true(not any(hasattr(ica, attr) for attr in run_time_attrs))
assert_not_equal(ica.labels_, None)
